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Conjoint.ly
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Conjoint.ly

Overview
Synopsis

Conjoint.ly is an online platform for discrete choice experimentation (conjoint analysis) for marketing, healthcare, and social science.

Category

Business Intelligence Software

Features

"Easy experimental design tool, including advanced conjoint design settings,
Automated participant management (including sample size calculation and reminders for respondents),
Segmentation of customer base based on revealed preferences from Hierarchical Bayes modelling,
Export of findings into PowerPoint and Excel, including market simulation modelling,
Survey customisations, including profiling questions, redirects, survey logic, mobile-ready templates."

Price

USD1,000 per generic conjoint experiment (most common type of discrete choice experiments), USD3,000 for alternative-specific ("brand-specific") experiment, completely free for students and academics

Pricing

per experiment run

Free Trial

Any experiment for up to 40 responses

Users Size

Startups, any size of corporates

User Industry

Market Research, Marketing Analytics

Support

Free

Languages

English

Countries

Worldwide

Devices

User interface: desktop, respondent inteface: desktop, mobile, tablet

OS

SaaS (works in a browser)

Try Free Trial

Any experiment for up to 40 responses

Website
Rating
Our Rating
User Rating
Ease of use
7.6
Features & Functionality
7.6
Advanced Features
7.6
Integration
7.6
Customer Support
7.6
Performance
7.6
Training
Implementation
Renew & Recommend
Bottom Line

Conjoint.ly is a SaaS solution fo rconjoint analysis aimed at managers who want to understand what factors drive customer demand
.

7.6
Our Rating
User Rating
You have rated this

Conjoint.ly helps managers understand what drives customer behaviour through surveys that simulate the act of purchasing (or any other type of choice-making). It offers a complete online solution from experiment set-up to data analysis and presentation of reports on marginal willingness to pay, market share simulation, segmentation, and more. Conjoint.ly uses discrete choice experimentation, which is sometimes referred to as choice-based conjoint. DCE is a more robust technique consistent with random utility theory and has been proven to simulate customers’ actual behaviour in the marketplace (Louviere, Flynn & Carson, 2010 cover this topic in detail). However, the output on relative importance of attributes and value by level is aligned ot the output from conjoint analysis (partworth analysis).

Conjoint.ly uses the attributes and levels specified in the interface to create an unlabelled choice design based on a D-optimal linear design (Kuhfeld, 2010). If the number of choice sets is excessive, the experiment is split into multiple blocks. Each choice set consists of several product construct alternatives and, by default, one “do not buy” alternative.Minimum sample size. Conjoint.ly automatically recommends a minimum sample size. In most cases, it is between 50 and 300 valid responses (which means you will need to invite several hundred respondents given that probably not everyone will take part). In our calculations, we use a proprietary formula that takes into account the number of attributes, levels, and other experimental settings. Conjoint.ly estimates a hierarchical bayesian (HB) multinomial logit model of choice using responses deemed valid. The value (partworth) of each level reflects how strongly that level sways the decision to buy the construct. Attributes with large variations in the sway factor are deemed more important. Specifically, we calculate attribute importance and level value scores (partworth utilities) by taking coefficients from the estimated model and linearly tansforming them so that:in each attribute, the sum of absolute values of positive partworths equals the sum of absolute values of the negative ones, and in each attribute, the sum of the spreads (maximum minus minimum) of parthworths equals 100%.

For experiments where one of the attributes is price, Conjoint.ly estimates a separate logit model that allows the calculation of the marginal rate of substitution between the price attribute and the non-price attribute. Conjoint.ly also performs checks for appropriateness of calculation of the measure, taking into account both the experimental set-up and the received responses (for example, limiting MWTP calculation in cases where there is non-linerity in price).Market share simulation is performed using individual coeffients from the estimated HB multinomial logit model.Ranked list of product constructs. Conjoint.ly forms the complete list of product constructs using all possible combinations of levels and ranks them based on a score computed from the relative level value scores (partworths).Segmentation. Conjoint.ly segments the market based on the individual coeffients from the estimated HB multinomial logit model using k-means clustering. We provide the values of the Calinski-Harabasz criterion (to help choose the appropriate number of clusters) and the normalised (0 to 1) Dunn partition coefficient for fuzzy k-means (to help choose the appropriate number of clusters as well as to decide if segmentation at all is crisp and hence appropriate). We provide the same reporting for each segment.”

Conjoint.ly

 

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Ease of use
Features & Functionality
Advanced Features
Integration
Customer Support
Performance
Training
Implementation
Renew & Recommend

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